For digital twin hydraulic monitoring perception networks

traditional single-point time series prediction models fail to capture spatial relationships among the devices

and cause missing correlation features; Uncertainty issues arising from strong subjectivity in model structure and parameter design. To address these issues

this paper presents a multi-parameter time series prediction model for monitoring perception networks based on the Bayesian optimization and Hyperband (BOHB)

self-learning graph structures

and Bidirectional Long Short-Term Memory (BiLSTM) networks. First

a self-learning graph structure is generated to extract spatial features of the perception network using graph neural networks. Then

the bidirectional Long Short-Term Memory networks are used to extract temporal features

and the BOHB method is used to optimize hyperparameters and improve prediction accuracy. Finally

the model is applied to proactive predictions of future states of the monitoring perception network. We have verified that our new model has achieved optimization rates higher more than 4.35%

33.14%

20.47%

9.09% and 15.03% in R2

RMSE

MAE

MAPE and RMSRE respectively

enjoys higher accuracy and stronger generalization ability compared with a variety of previous prediction models

and has significant performance advantages.